Enhancing user experience in VR environments through AI-driven adaptive UI design
This paper presents a new approach to improving user experience in virtual reality (VR)
environments using AI-driven user interface (UI) design. The proposed system uses …
environments using AI-driven user interface (UI) design. The proposed system uses …
Optimization of High-Frequency Trading Strategies Using Deep Reinforcement Learning
This study presents a new method for optimising high-risk trading (HFT) strategies using
deep learning (DRL). We propose a multi-time DRL framework integrating advanced neural …
deep learning (DRL). We propose a multi-time DRL framework integrating advanced neural …
AI-driven solar energy generation and smart grid integration: A holistic approach to enhancing renewable energy efficiency
This paper comprehensively analyzes AI-driven solar energy generation and smart grid
integration, focusing on enhancing renewable energy efficiency. The study examines …
integration, focusing on enhancing renewable energy efficiency. The study examines …
Artificial intelligence-based inventory management for retail supply chain optimization: a case study of customer retention and revenue growth
This study explores the evolution of AI-driven product management in the retail industry,
focusing on product quality, customer retention, and revenue growth. From the extensive …
focusing on product quality, customer retention, and revenue growth. From the extensive …
Leveraging Large Language Models for Context-Aware Product Discovery in E-commerce Search Systems
This study presents a new way to improve product discovery in e-commerce research using
large-scale language models (LLMs) for content-aware instruction. We propose a new …
large-scale language models (LLMs) for content-aware instruction. We propose a new …
Optimizing Intelligent Edge Computing Resource Scheduling Based on Federated Learning
This study proposes a novel federated learning framework for optimizing intelligent edge
computing resource scheduling. The framework addresses the challenges of device …
computing resource scheduling. The framework addresses the challenges of device …
Enhancing personalized search with ai: a hybrid approach integrating deep learning and cloud computing
This paper presents a novel hybrid approach for enhancing personalized search by
integrating deep learning techniques with cloud computing infrastructure. The proposed …
integrating deep learning techniques with cloud computing infrastructure. The proposed …
Detection of network security traffic anomalies based on machine learning KNN method
This paper discusses the application and advantages of machine learning in anomaly
detection of network security traffic. By summarizing the existing methods and techniques of …
detection of network security traffic. By summarizing the existing methods and techniques of …
A deep learning approach for optimizing monoclonal antibody production process parameters
This study presents a new deep learning method for optimizing monoclonal antibody (mAb)
production processes using a hybrid Convolutional Neural Network-Long Short-Term …
production processes using a hybrid Convolutional Neural Network-Long Short-Term …